Pre-installed Computer Vision Software
Contains the OpenVINO(TM) toolkit for hardware acceleration of deep learning inference for computer vision applications.
Hardware Acceleration
Harness the performance of Intel®-based accelerators for deep learning inference with the CPU, GPU, and VPU included in this kit.
Reduce Time to Field Trial
The kit includes a field-ready mountable aluminum enclosure and camera. Purchase available Wi-Fi and cellular module add-ons.
Benefits of the OpenVINO™ toolkit
Accelerate Performance
Access Intel computer vision accelerators. Speed code performance.
Supports heterogeneous processing & Asynchronous execution.
Integrate Deep learning
Unleash convolutional neural network (CNN) based deep learning inference across using a common API & 10 trained models.
Speed development
Reduce time using a library of optimized OpenCV* & OpenVX* functions, 15+ samples.
Develop once, deploy for current & future Intel-based devices.
Innovate & customize
Use the increasing repository of OpenCL™ starting points in OpenCV* to add your own unique code.
UP Squared AI Vision X Developer kit
What's in the box |
A VESA Mountable edge computer with Intel Atom®X7-E3950 processor, 8GB memory, 64 GB eMMC with Ubuntu image (kernel 4.15) OpenVINO™ toolkit R5 |
Power supply 5V @ 6A |
EU/US Power cord |
AI Core X integrated with Intel® Movidius™ Myriad™ X (version B) |
USB Camera with maximum resolution of 1920 x 1080p at 30 fps |
Software |
Ubuntu 16.04 Desktop |
OpenVINO™ toolkit R5 |
Intel® System Studio 2018 Community Edition with Eclipse IDE |
Drivers for Intel® VTune™ Amplifier, Intel® Energy Profiler, Intel® Graphics Performance Analyzer |
MRAA and UPM I/O and sensor libraries for C++, Python, Java and JavaScript |
USB Vision camera
Maximum resolution | Full HD 1920 x 1080 |
Supported video quality | 320 x 240 / 352 x 288 / 640 x 480 VGA / 800 x 600 MJPEG @ 60 fps 1024 x 768 / 1280 x 720 / 1280 x 1024 / 1920 x 1080 MJPEG @ 30 fps |
Picture format | MJPEG / YUV2 (YUYV) |
Focus motor | Manual focus |
Sensor | 1/2.7" OV2735 |
Mini illumination | 0.05 LUX |
Interface | USB 2.0 High Speed |
Lens | F3.6mm |
Support OTG | USB 2.0 OTG |
Support free driver | USB Video Class (UVC) |
Dimensions | 41 x 41 x 41.5 mm / 245g |
Exposure | Auto |
Auto white balance | Auto |
AGC | Support |
Working temperature | -20°C to 80°C |
Operating humidity | 30%~90% Rh |
Working power | DC 5V |
Cable length | 1 meter |
AI Core X Specifications
UP AI Core X | |
---|---|
SoC | Intel® Movidius™ Myriad™ X VPU 2485 |
Quantity of VPU | 1 |
Form factor | Mini PCI-Express |
Memory | 4Gb LPDDR4 x1 |
Dimensions | 30 x 51 mm (1.18" x 2.00") |
Thermal | Fanless heatsink |
Supported Frameworks | Caffe, TensorFlow, MXNet, Kaldi, ONNX |
Minimum system requirements | x86_64 Computer running Ubuntu 16.04, 1GB memory, 4GB Free storage, vacant expansion slot |
Software tool | Intel® Distribution of OpenVINO™ toolkit R4 or above rev. |
UP Squared Specifications
Soc | Intel® Atom™ x7-E3950 (up to 2GHz) |
Graphics | Intel® Gen 9 HD |
Video and Audio | 1x HDMI 1.4b 4K@30Hz, 1x DP 1.2 4K @ 60Hz |
Camera interface | CSI 2-lane + CSI 4-lane (only pass-through) |
Memory | 8GB |
Storage | 64GB |
USB | 3x USB3.0 (Type A) 1x USB 3.0 OTG (micro B) 2x USB2.0 + 2x UART (Tx/Rx) |
Ethernet | 2x Gb Ethernet full speed RJ-45 |
RTC | Yes |
Expansion | 40 pin General Purpose bus 60 pin EXHAT 1x mini-PCI-e, M.2 2230, SATA3 |
OS Support | MS Windows 10 (full) MS Windows IoT Core Linux (ubilinux, Ubuntu, Yocto) Android Marshmallow |
Dimensions | 85.6 x 90 mm |